We use cookies to personalise content, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners If you want to change your cookie setting, please see the ‘how to reject cookies’ section of our Cookies Notice. Otherwise, if you agree to our use of cookies, please continue to use our website. To learn more, view our Cookies Notice.

Why Data Science Should be Part of Your SEO Strategy

It seems like conversations around Machine Learning and Data Science and how these 'buzzwords' will continue to affect the industry are happening more frequently. These advancements are and will continue to be important for both digital marketing and SEO. Before we delve into the 'how', let's look at two core concepts:

1. Data Science: Data is being generated every day to provide businesses with key opportunities that lead to better decisions. A relevant example is the creation of Netflix's award-winning show, House of Cards. Through in-depth data analysis, Netflix uncovered a strong crossover: viewers who watched movies with Kevin Spacey as a lead character also watched political dramas and films directed by David Fincher. This winning formula ultimately paved the way for the initial success of House of Cards.

2. SEO: In its strictest definition, SEO is about understanding how an algorithm (think: Google) ranks different elements (think: websites) and improving input metrics to get the website in a higher position for the target topics.

Historically, there are two primary challenges that SEO, as a discipline, has always had:

1. Lack of clarity: search engines won't share the formula on how their algorithm works, but this is something that is changing all the time.

2. Third-party metrics: for a good understanding of how different parts of the ranking algorithm perform (in comparison to the competitors), one needs to use a wide variety of tools.

Until some time ago, one would approach SEO using a combination of best practice and linear thinking i.e. we have fewer links than sites in higher positions so let's work on getting more links. Although this might work for less competitive verticals, a more mature approach is needed in most instances and this is where data science comes in.

Data science allows us to combine various data sets and see which variable is likely to make the biggest impact. It all starts with formulating hypotheses, and selecting the right data sources and the metrics that these sources provide. This could be backlink information from MajesticSEO, state of information architecture from Ryte or site load time from PageSpeed Insights or Lighthouse. The information gathered must be against both high and low performers to have enough data to see what exactly the differences are between the winners and non-winners.

This is where data models that give insights on where the opportunities come in. These come with a level of confidence that signifies the likelihood of the change making a significant improvement. Once the changes are implemented, the model is revisited. Where needed, adjustments are made to create an ongoing loop of data-driven approaches that rely on science and not a 'gut feeling'.

Keep in mind, search algorithms are different by country and vertical, and are constantly changing the models for each client.

Conclusion

Our dedicated team of experts are utilising data science to deliver better results for our clients by combining different data sets and leveraging proprietary tools to extract actionable insights for better ROI. Get in touch with the team if you have any questions around data science and SEO.